Multi-agent systems performance by adaptive/non-adaptive agent selection

Toshiharu Sugawara; Kensuke Fukuda; Toshio Hirotsu; Shin Ya Sato; Satoshi Kurihara

doi:10.1109/IAT.2006.93

Multi-agent systems performance by adaptive/non-adaptive agent selection

Toshiharu Sugawara^*, Kensuke Fukuda, Toshio Hirotsu, Shin Ya Sato, Satoshi Kurihara

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

Our research interest lies in studing how local strategies about partner agent selection using reinforcement learning with variable exploitation-versus- exploration parameters influence the overall efficiency of multi-agent systems (MAS). An agent often has to select appropriate agents to assign tasks that are not locally executable. Unfortunately no agent in an open environment can understand the all states of all agents, so this selection must be done according to local information. In this paper we investigate how the overall performance of MAS is affected by their individual learning parameters for adaptive partner selections for collaboration. We show experimental results using simulation and discuss why the overall performance of MAS varies.

Original language	English
Title of host publication	Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06
Publisher	IEEE Computer Society
Pages	555-559
Number of pages	5
ISBN (Print)	9780769527482
DOIs	https://doi.org/10.1109/IAT.2006.93
Publication status	Published - 2006
Externally published	Yes
Event	2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'06 - Hong Kong, China Duration: 2006 Dec 18 → 2006 Dec 22

Publication series

Name	Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06

Conference

Conference	2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'06
Country/Territory	China
City	Hong Kong
Period	06/12/18 → 06/12/22

ASJC Scopus subject areas

Computer Networks and Communications
Software

Access to Document

10.1109/IAT.2006.93

Cite this

Sugawara, T., Fukuda, K., Hirotsu, T., Sato, S. Y., & Kurihara, S. (2006). Multi-agent systems performance by adaptive/non-adaptive agent selection. In Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06 (pp. 555-559). Article 4052976 (Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06). IEEE Computer Society. https://doi.org/10.1109/IAT.2006.93

Multi-agent systems performance by adaptive/non-adaptive agent selection. / Sugawara, Toshiharu; Fukuda, Kensuke; Hirotsu, Toshio et al.
Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06. IEEE Computer Society, 2006. p. 555-559 4052976 (Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Sugawara, T, Fukuda, K, Hirotsu, T, Sato, SY & Kurihara, S 2006, Multi-agent systems performance by adaptive/non-adaptive agent selection. in Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06., 4052976, Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06, IEEE Computer Society, pp. 555-559, 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology, IAT'06, Hong Kong, China, 06/12/18. https://doi.org/10.1109/IAT.2006.93

Sugawara T, Fukuda K, Hirotsu T, Sato SY, Kurihara S. Multi-agent systems performance by adaptive/non-adaptive agent selection. In Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06. IEEE Computer Society. 2006. p. 555-559. 4052976. (Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06). doi: 10.1109/IAT.2006.93

Sugawara, Toshiharu ; Fukuda, Kensuke ; Hirotsu, Toshio et al. / Multi-agent systems performance by adaptive/non-adaptive agent selection. Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06. IEEE Computer Society, 2006. pp. 555-559 (Proceedings - 2006 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2006 Main Conference Proceedings), IAT'06).

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abstract = "Our research interest lies in studing how local strategies about partner agent selection using reinforcement learning with variable exploitation-versus- exploration parameters influence the overall efficiency of multi-agent systems (MAS). An agent often has to select appropriate agents to assign tasks that are not locally executable. Unfortunately no agent in an open environment can understand the all states of all agents, so this selection must be done according to local information. In this paper we investigate how the overall performance of MAS is affected by their individual learning parameters for adaptive partner selections for collaboration. We show experimental results using simulation and discuss why the overall performance of MAS varies.",

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AB - Our research interest lies in studing how local strategies about partner agent selection using reinforcement learning with variable exploitation-versus- exploration parameters influence the overall efficiency of multi-agent systems (MAS). An agent often has to select appropriate agents to assign tasks that are not locally executable. Unfortunately no agent in an open environment can understand the all states of all agents, so this selection must be done according to local information. In this paper we investigate how the overall performance of MAS is affected by their individual learning parameters for adaptive partner selections for collaboration. We show experimental results using simulation and discuss why the overall performance of MAS varies.

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Multi-agent systems performance by adaptive/non-adaptive agent selection

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